NSF BREAD
The PhenoApps project will converge novel advances in image processing and machine vision to deliver transformative mobile applications through established breeder networks. User-friendly mobile apps for field-based high-throughput phenotyping that utilize novel image analysis algorithms will be built and deployed to model and extract plant phenotypes. To ensure both immediate, broad deployment and functionality on a diverse set of crops, breeder networks for cassava and wheat will be engaged, providing a diverse set of target plant phenotypes, environments, breeding programs and working cultures.
Contents
Summary
Overview
Dramatic increases in the speed and ability to collect precision phenotypic data are needed to decipher plant genomes and accelerate plant breeding. Over the past decade, the availability of genomic data has exploded while the methods to collect phenotypes have made minimal advancements. This has led to a dramatic imbalance in data sets connecting genotype to phenotype and highlighting phenotyping as the remaining major bottleneck in plant breeding programs. This project will advance the field of 3D graphics and modeling, data mining, and deep learning through integration of simultaneous ground truth phenotypic measurements and imaging with mobile technology.
By focusing on novel algorithms delivered through mobile apps, innovative phenotyping tools can be rapidly deployed through readily available and highly-penetrant mobile technology. This approach will enable rapid dissemination and broad usability. Collectively equipping thousands of breeders around the world with tools for rapid collection, processing and analysis of complex phenotypes will provide the foundation for increasing genetic gain that will ultimately result in improved productivity, food security, nutrition, and income of smallholder farmers and their families in developing countries.
Workflow
Add graphical app workflow (prelim data → algorithms → beta users → feedback and changes → implementation → beta test → release)
Project team
Add people and hierarchy chart (research teams, breeders, developers, etc.)
Wheat
Summary
Cycle
Traits
| Trait | Priority | Status |
|---|---|---|
| Spike count | 0 | None |
| Rust quantification | 0 | None |
| Seed size and shape | 0 | 1KK |
| Leaf morphology | 0 | None |
| Plant architecture | 0 | None |
| Plant physiology | 0 | None |
Cassava
Summary
Cycle
Traits
| Trait | Priority | Status |
|---|---|---|
| Root size and shape | 0 | 1KK |
| Cassava mosaic disease | 0 | None |
| Cassava brown streak disease | 0 | None |
| Whitefly count | 0 | None |
| Leaf morphology | 0 | None |
| Plant architecture | 0 | None |
| Plant physiology | 0 | None |
Progress
Field Book
Field Book was developed to eliminate paper note-taking from plant breeding programs and facilitate robust data collection and rapid data access. It is a standalone program that utilizes a straightforward interface that focuses on a single entry and trait at a time. The interface is dynamic and changes based on the type of trait being collected. Data can be analyzed the same day they are collected, resulting in the ability to find and fix any mistakes made when collecting data. Field Book has been adopted by many U.S. and international breeding programs. It is the primary data collection software used by the Triticeae Coordinated Agriculture Project, the [www.nextgencassava.org NextGen Cassava] project, many universities (KSU, Cornell, UNL, etc.), and even many private companies (Syngenta, Limagrain, and Bayer). As of April 2016, more than 1100 devices around the world have an active installation of Field Book.
1KK
1KK is an app designed to analyze seed lots. Its name comes from the one thousand kernel weight that is commonly used as a selection criteria in plant breeding programs. 1KK extracts seed morphology from images captured by phone and tablet cameras. A non-parametric algorithm is used to identify individual seeds for shape measurements. Reference circles of known size included on the background translate pixel measurements of seeds to actual size. Each individual seed length, width, and area are determined using the same algorithm implemented in SmartGrain. Data can be exported in a sample summary form and on a per-object basis. For measurement of thousand kernel weight, the total number of seeds are counted and divided by the total weight. For weight measurements, the app is compatible with Elane USB scales (1g resolution).
